Recommendation systems are widely deployed, and are a critical component of the success of such established technology companies as Amazon and Netflix, as well as newcomers such as Pandora. It is highly desirable to tell users why a recommendation is being made. Such an explanation increases users' assessment of the quality of the recommendations, and incurs forgiveness when a bad recommendation is being made. However, the best recommendations are often made by complex machine learning systems, which can employ algorithms that do not lend themselves to human-interpretable explanations. Therefore, there is an impasse in which companies often elect not to use the best recommendation algorithms, or suffer the consequences of not explaining recommendations to users.
Accordingly, it would be useful to have a way to use the best recommendation engines available, while offering users a plausible explanation for the recommendations being made.